Risk factors of decisional conflict among people living with chronic pain identified through a pan-Canadian survey
Bibliographic record
Abstract
Making decisions about chronic pain care is often challenging due to uncertainties, leading to decisional conflict when individuals do not receive the support and information they need. Shared decision-making interventions can help meet these needs; however, their effectiveness is inconsistent in the context of chronic pain. This study aimed to identify the decisional needs influencing decisional conflict among adults with chronic pain in Canada, to guide the development of more comprehensive interventions. In this pan-Canadian online survey, we measured decisional conflict related to the most difficult decision using the Decisional Conflict Scale (≥ 37.5 indicating clinically significant conflict) and assessed decisional needs based on the Ottawa Decision Support Framework. Of the 1,649 participants, 1,373 reported a Decisional Conflict Scale score. The mean age was 52 (SD = 16.4), with half of respondents being men (49.5%) and pain duration ranging from 3 months to 59 years. One-third (33.7%) experienced clinically significant decisional conflict. Seventeen risk factors were identified, including difficulty understanding healthcare information (OR = 2.43) and lack of prior knowledge of available options (OR = 2.03), while role congruence in decision-making was associated with reduced conflict (OR = 0.57). Future SDM interventions could be enhanced by targeting multiple risk factors of decisional conflict.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".